In [1]}, the attack infers the participant's training dataset from the gradients they share during training. The authors develop a gradient-based feature reconstruction attack, in which the attacker receives the gradient update from a participant and aims to steal their training set. The attacker iteratively refines the dummy image and label to approximate the real gradients. When they converge, the dummy training data converges to the real one with high confidence. [2]} proposes a taxonomy with the different attacks threatening an FL model. The taxonomy is organized into tables with defenses and attacks. Attacks include the description and the source of the vulnerability that it exploits. On the other hand, [3]} creates a flowchart-like visual representation of attacks and countermeasures. However, it only breaks attacks into data privacy and model performance categories.
